Detection of fungal infection and Ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging |
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| Affiliation: | 1. Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, Canada, R3T 5V6;2. Agriculture and Agri-Food Canada, c/o Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, Canada, R3T 5V6;3. Canadian Grain Commission, c/o Richardson Centre for Functional Foods and Nutraceuticals, University of Manitoba, Winnipeg, MB, Canada, R3T 2E1;1. INRA, UR1268 Biopolymères Interactions Assemblages, F-44316 Nantes, France;2. INRA, UMR AGAP 1334 Amélioration Génétique et Adaptation des plantes, F-34060 Montpellier, France;3. INRA, UR1264 Mycologie et Sécurité des Aliments (MycSA) - QUALIS, CS 20032, F-33882 Villenave d''Ornon, France;4. Data_frame, 25 rue Stendhal, F-44300 Nantes, France;1. Embrapa Agricultural Informatics, Av. André Tosello, 209, C.P. 6041, Campinas 13083-886, SP, Brazil;2. Embrapa Wheat, Rodovia BR-285, Km 294, C.P. 3081, Passo Fundo 99001-970, RS, Brazil;1. Department of Biosystems Engineering, Room E2-376, Engineering, Information and Technology Complex, 75A Chancellor''s Circle, University of Manitoba, Winnipeg, Manitoba R3T 2N2, Canada;2. Cereal Research Centre, AAFC, Department of Biosystems Engineering, Engineering, Information and Technology Complex, 75A Chancellor''s Circle, Winnipeg, Manitoba R3T 2N2, Canada;1. College of Engineering, China Agricultural University, No. 17 Tsinghua East Road, Beijing 100083, China;2. Quality & Safety Assessment Research Unit, Richard B. Russell Research Center, USDA-ARS, 950 College Station Road, Athens, GA 30605, USA;3. Crop Genetics and Breeding Research Unit, USDA-ARS, 2747 Davis Road, Tifton, GA 31793, USA |
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| Abstract: | A study was done to detect Aspergillus glaucus, and Penicillium spp., infection and Ochratoxin A contamination in stored wheat using a Near-Infrared (NIR) Hyperspectral Imaging system. Fungal-infected samples were imaged every two weeks, and the three dimensional hypercubes obtained from image data were transformed into two dimensional data. Principal component analysis was applied to the two dimensional data and based on the highest factor loadings, 1280, 1300, and 1350 nm were identified as significant wavelengths. Six statistical features and ten histogram features corresponding to the significant wavelengths were extracted and subjected to linear, quadratic and Mahalanobis discriminant classifiers. All the three classifiers differentiated healthy kernels from fungal-infected kernels with a classification accuracy of more than 90%. The quadratic discriminant classifier provided classification accuracy higher than the linear and Mahalanobis classifiers for pair-wise, two-way and six-way classification models. The Ochratoxin A contaminated samples had a unique significant wavelength at 1480 nm in addition to the two significant wavelengths corresponding to fungal infection. The peak at 1480 nm was identified only in the Ochratoxin A contaminated samples. The Ochratoxin A contaminated samples can be detected with 100% classification accuracy using NIR hyperspectral imaging system. The NIR hyperspectral system can differentiate between different fungal infection stages and different levels of Ochratoxin A contamination in stored wheat. |
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| Keywords: | Near-infrared (NIR) hyperspectral imaging Principal component analysis Discriminant analysis Storage mold |
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